Optimized CNN for Cardiac Disease Detection Using Hybrid Crow Search and Dragonfly Algorithms

Cardiovascular Disease (CVD) is a hazardous condition for humans that is rapidly expanding around the globe in developed as well as developing nations, eventually resulting in death. In this disease, the heart often fails to supply sufficient oxygen to other parts of the body so that they are cannot able to perform their normal activities. It is critical to identify this problem immediately and precisely in order to save patients lives and avoid additional damage.Henceforth, this work proposes an efficient image processing strategy based on a hybrid algorithm optimised Convolutional Neural Network (CNN) classifier, which is used in this present research for precise identification of cardiac vascular disease.In the beginning, the Electrocardiogram (ECG) images are obtained and processed by removing noise using an adaptive median filter. The pre-processed ECG image is then divided into different regions using the Fuzzy C-Means (FCM) algorithm, which improves the accuracy of heart illness detection.Following segmentation, the Grey level Co-occurrence Matrix (GLCM) is employed to efficiently extract high-ranked features. Subsequently, the characteristics are considerably identified using a novel hybrid Crow Search Optimization (CSO) Dragon Fly Optimisation (DFO) algorithm-based CNN classifier for optimally categorising the cardiacvascular illness.The entire work is validated in Python software, and the results show that the novel method produces the best possible outcomes with a maximum precision of 98.12%.